Ebola impact and quarantine in a network model

TL;DR

This paper explores how network-based SIR models can better understand Ebola outbreaks by analyzing the effects of community connectivity patterns on disease impact and duration.

Contribution

It introduces a network framework for SIR models to study Ebola spread, emphasizing the role of connectivity patterns in outbreak dynamics.

Findings

Connectivity patterns significantly influence outbreak impact and duration.

Optimizing network parameters can potentially control disease spread.

Modeling communities as coupled nonlinear systems enhances understanding of contagion dynamics.

Abstract

Much effort has been directed towards using mathematical models to understand and predict contagious disease, in particular Ebola outbreaks. Classical SIR (susceptible-infected-recovered) compartmental models capture well the dynamics of the outbreak in certain communities, and accurately describe the differences between them based on a variety of parameters. However, repeated resurgence of Ebola contagions suggests that there are components of the global disease dynamics that we don't yet fully understand and can't effectively control. In order to understand the dynamics of a more widespread contagion, we placed SIR models within the framework of dynamic networks, with the communities at risk of contracting the virus acting as nonlinear systems, coupled based on a connectivity graph. We study how the effects of the disease (measured as the outbreak impact and duration) change with respect to local parameters, but also with changes in both short-range and long-range connectivity patterns in the graph. We discuss the implications of optimizing both these measures in increasingly realistic models of coupled communities.